For questions related to the advantage actor-critic algorithms (that is, actor-critic algorithms that use the "advantage" function).

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What does the notation $\hat{A}_t\left(s_{0: \infty}, a_{0: \infty}\right)$ appearing in Generalized Advantage Estimation mean?

In general, the advantage function is defined as: $A^\pi\left(s_t, a_t\right):=Q^\pi\left(s_t, a_t\right)-V^\pi\left(s_t\right)$ So far, I understand this formular like this: With the advantage ...
• 185
41 views

Why is the loss function for the critic decreasing and the gradient increasing over the episodes?

I have built an Actor Critic model, where we have a Quantile Critic. The aim of the task is to tell what is the optimal portfolio choice of an agent based on quantiles. The model set up: We have ...
21 views

Can A2C deal with a reward that is decided later than action selection?

I am trying to use a policy gradient based RL algorithm, A2C. However, my training case is slightly different from what typical training tragets are. In my case, a reward is given not immediately ...
117 views

Action space for the A2C algorithm

I am working on a problem where I need to generate a list of 0 - 8 different prices as the action space where generating 0 prices represents doing nothing ...
72 views

A2C unable to solve Cartpole

I have coded my own A2C implementation using PyTorch. However, despite having followed the algorithm pseudo-code from several sources, my implementation is not able to achieve a proper Cartpole ...
• 26
177 views

Why does Advantage Learning help function approximators?

Many later RL algorithms like PPO or Duelling DQN estimate the advantage. I am not very sure of how that really helps. For instance, the actor loss for a simple actor critic algorithm is given by - <...
3k views

What is the difference between A2C and Q-Learning, and when to use one over the other?

I'm trying to get an accurate answer about the difference between A2C and Q-Learning. And when can we use each of them?
• 33
1 vote
350 views

How to tune hypeparametes in A2C-ppo?

Im currently working with A2C. The model was able to learn open ai pong, i ran this as a sanity check that i havent made any bugs. Now im trying to make the model play breakout, but still after 10m ...
1 vote
87 views

Can vanilla multi armed bandit problems be solved by RL algorithms like A2C and PPO?

Let's say we have N bandit machines with some distributions (assume some are gaussian, some are uniform, some are chi squared). We want to maximize rewards in X amount of time. I am aware that ...
487 views

A2C: Why do episode rewards reset?

I am training a model using A2C with stable baselines 2. When I increased the timesteps I noticed that episode rewards seem to reset (see attached plot). I don´t understand where these sudden decays ...
• 21
34 views

How can I compare the results of AC1 with the results of A3C (on the CartPole environment)?

I am implementing A3C for the CartPole environment. I want to compare the results I got from A3C with the ones I got from AC1. The problem is I don't know which process to look at. If I use, let's say,...
684 views

i'm trying to solve a problem in which i need to carry out reinforcement learning with multiple simultaneous actions in continuous action space . i checked the multiagent structure; however, im trying ...
1 vote
40 views

What is the difference between step_model and train_model in the OpenAI implementation of the A2C algorithm?

I'm struggling a little with understanding the OpenAI implementation of A2C in the baselines (version 2.9.0) package. From my understanding, one ...
3k views

What is the difference between vanilla policy gradient with a baseline as value function and advantage actor-critic?

What is the difference between vanilla policy gradient (VPG) with a baseline as value function and advantage actor-critic (A2C)? By vanilla policy gradient I am specifically referring to spinning up's ...
• 125
1 vote
556 views

Why is the "reward to go" replaced by Q instead of V, when transitioning from PG to actor critic methods?

While transitioning from simple policy gradient to the actor-critic algorithm, most sources begin by replacing the "reward to go" with the state-action value function (see this slide 5). I am not ...
• 301
877 views

What is the advantage of using more than one environment with the advantage actor-critic?

make_env = lambda: ptan.common.wrappers.wrap_dqn(gym.make("PongNoFrameskip-v4")) envs = [make_env() for _ in range(NUM_ENVS)] Here is a code you can look at. ...
• 261
416 views

What is the original source of the TD Advantage Actor-Critic algorithm?

What is the original source of the TD Advantage Actor-Critic algorithm? I found this tutorial really helpful for learning the algorithm. However, what is the original source of this algorithm?
• 121
210 views

Is A2C loss function taking smaller steps for larger mistakes?

A2C loss is usually defined as advantage * (-log(actor_predictions)) * target where target is a one-hot vector (with some ...
• 161
1k views

How to set the target for the actor in A2C?

I did a simple Actor-Critic implementation in Keras using 2 networks where the critic learns the Q-Values of every action, and the actor predicts probabilities for choosing each action. In training, ...
• 161
1k views

Why I got the same action when testing the A2C?

I'm working on an advantage actor-critic (A2C) reinforcement learning model, but when I test the model after I trained for 3500 episodes, I start to get almost the same action for all testing episodes....
1k views

Implementation of PPO - Value Loss not converging, return plateauing

Copy from my Reddit post: (Sorry if this does not fit here, please tell me and I delete it) Help regarding I'm working on an implementation of PPO, which I plan to use in my (Bachelors) Thesis. To ...
• 131
575 views

What is the difference between A2C and running an agent in an environment vector?

I've implemented A2C. I'm now wondering why would we have multiple actors walk around the environment and gather rewards, why not just have a single agent run in an environment vector? I personally ...
• 471